code: def __call__(
      self,
      scenario_dict,
      seed,
      ta,
      ta_kwargs,
      metalearning_configurations,
      n_jobs,
      dask_client,
      multi_objective_algorithm,
      multi_objective_kwargs,
  ):
      from smac.facade.smac_ac_facade import SMAC4AC
      from smac.intensification.simple_intensifier import (
          SimpleIntensifier,
      )
      from smac.runhistory.runhistory2epm import RunHistory2EPM4LogCost
      from smac.scenario.scenario import Scenario
  
      scenario = Scenario(scenario_dict)
  
      initial_configurations = []
      for member in self.portfolio.values():
          try:
              hp_names = scenario.cs.get_hyperparameter_names()
              _member = {
                  key: member[key] for key in member if key in hp_names
              }
              initial_configurations.append(
                  Configuration(
                      configuration_space=scenario.cs, values=_member
                  )
              )
          except ValueError:
              pass
  
      rh2EPM = RunHistory2EPM4LogCost
      return SMAC4AC(
          scenario=scenario,
          rng=seed,
          runhistory2epm=rh2EPM,
          tae_runner=ta,
          tae_runner_kwargs=ta_kwargs,
          initial_configurations=initial_configurations,
          intensifier=SimpleIntensifier,
          run_id=seed,
          n_jobs=n_jobs,
          dask_client=dask_client,
          multi_objective_algorithm=multi_objective_algorithm,
          multi_objective_kwargs=multi_objective_kwargs,
      )
  
imports: from __future__ import annotations
  from typing import Any, Dict, List, Mapping, Optional, Union
  import hashlib
  import json
  import os
  import pathlib
  import pickle
  import dask.distributed
  import numpy
  import pandas
  import scipy.sparse
  import sklearn
  from ConfigSpace import Configuration
  import autosklearn
  import autosklearn.experimental.selector
  from autosklearn.classification import AutoSklearnClassifier
  from autosklearn.ensembles.abstract_ensemble import AbstractEnsemble
  from autosklearn.ensembles.ensemble_selection import EnsembleSelection
  from autosklearn.metrics import Scorer, accuracy, balanced_accuracy, log_loss, roc_auc
label_desc: Use image node_id 2 for calling the SmacObjectCallback obj's underlying member method code with example usage: obj.__call__(scenario_dict, seed, ta, ta_kwargs, metalearning_configurations, n_jobs, dask_client, multi_objective_algorithm, multi_objective_kwargs) and returns: SMAC4AC